***Use Merged Data.dta***

**Table 1
tab attack noattack

**Table 2
*Model 1
logit type1 educ ln_age sex ib3.ethnic ib2.pid i.tnss, cluster(tnss)
*Model 2
melogit type1 educ ln_age sex ib3.ethnic ib2.pid growth DPP || tnss:, vce(cluster tnss)
*Model 3
melogit type1 educ ln_age sex ib3.ethnic ib2.pid growth DPP DPPgrowth || tnss:, vce(cluster tnss)

**Figure 5
melogit type1 educ ln_age sex ib3.ethnic ib2.pid c.growth##i.DPP||tnss:, vce(cluster tnss)
margins, dydx(growth) at(DPP=(0(1)1))
marginsplot, x(DPP) xtitle("0=KMT rules; 1=DPP rules")

**Figure 6
melogit type1 educ ln_age sex ib3.ethnic ib2.pid c.growth##i.DPP||tnss:, vce(cluster tnss)
margins, dydx(DPP) at(growth=(0(0.1)7))
marginsplot, x(growth) xtitle("Economic Growth Rate")

**Figure 7a
logit type1 educ ln_age sex ib3.ethnic ib2.pid i.tnss, cluster(tnss)
coefplot, xline(0) coeflabels(educ="education" ln_age="ln(age)" 1.sex="female" 2.ethnic="Hakka" 3.ethnic="mainlander" 1.pid="light Green" 2.pid="independent" 3.pid="light Blue" 4.pid="deep Blue" 2004.tnss="2004" 2005.tnss="2005" 2008.tnss="2008" 2011.tnss="2011" 2012.tnss="2012" 2013.tnss="2013" 2014.tnss="2014" 2015.tnss="2015" 2016.tnss="2016" 2017.tnss="2017" 2019.tnss="2019" 2020.tnss="2020") drop(_cons) baselevels

**Figure 7b
melogit type1 educ ln_age sex ib3.ethnic ib2.pid growth DPP || tnss:, vce(cluster tnss)
coefplot, xline(0) coeflabels(educ="education" ln_age="ln(age)" 1.sex="female" 2.ethnic="Hakka" 3.ethnic="mainlander" 1.pid="light Green" 2.pid="independent" 3.pid="light Blue" 4.pid="deep Blue" growth="Econ Growth" DPP="DPP Rule") drop(_cons) baselevels

**Figure 7c
melogit type1 educ ln_age sex ib3.ethnic ib2.pid growth DPP DPPgrowth || tnss:, vce(cluster tnss)
coefplot, xline(0) coeflabels(educ="education" ln_age="ln(age)" 1.sex="female" 2.ethnic="Hakka" 3.ethnic="mainlander" 1.pid="light Green" 2.pid="independent" 3.pid="light Blue" 4.pid="deep Blue" growth="Econ Growth" DPP="DPP Rule" DPPgrowth="Growth*DPP") drop(_cons) baselevels